About

Idris Adjerid is an associate professor in Business Information Technology at the Pamplin College of Business at Virginia Tech. He received his Ph.D. in information systems and management from Carnegie Mellon University and earned both an MBA and a bachelor’s degree in business information technology from Virginia Tech. His research uses econometric methods as well as lab and field experiments and consists of two, often overlapping, streams. The first stream focuses on the economics of privacy, with a focus on the intersection of behavioral economics and privacy decision-making. The second stream focuses on the economics of health care technologies. His work has been published in leading journals, including Management Science, Information Systems Research, MIS Quarterly, American Psychologist, and ACM Computing Surveys. His research and expert commentary have been cited by numerous outlets in the popular press, including The New York Times, the Wall Street Journal, the Washington Post, Wired, Politico, and USA Today.

Research topics

• Computer Security
• Computer Science
• Business
• Political Science
• Internet privacy
• Economics
• Marketing
• Industrial organization
• Risk analysis (engineering)
• International trade
• Psychology
• Public relations
• Law and economics

Selected publications

• Breaking Bad Email Habits: Bounding the Impact of Simulated Phishing Campaigns

  Open MIND · 2026-03-04

  preprint

  Simulated phishing campaigns are widely deployed, yet the behavioral data they produce is endogenous: because training is triggered by clicking, the employees receiving intervention have already demonstrated susceptibility. This endogeneity, combined with the difficulty of separating genuine habit formation from stable individual differences, means standard analyses can mischaracterize program effectiveness. In this Research Note, we develop a generalizable analytic framework addressing both biases simultaneously. We utilize marginal structural models (MSMs) to correct for the endogenous, click-triggered assignment of training, while integrating correlated random effects (CRE) to disentangle true state dependence from stable employee heterogeneity. Applying the MSM+CRE estimator to logs from 17 campaigns delivered to university staff (192,840 observations) reveals that analyses ignoring stable differences overstate the causal persistence of clicking; most repeat clicking reflects who employees are, not the effect of recent failures. This persistence is context-dependent, amplifying when successive campaigns share persuasion cues. Teachable-moment features also matter: emotion framing and explicit reporting pitches can largely eliminate persistence, while annotated-email cues modestly exacerbate it. Finally, employees engaging with the education page exhibit greater persistence than those dismissing it, consistent with an emboldening mechanism. We contribute methodologically by integrating MSMs and CRE into a portable framework for analyzing standard simulation logs, and practically by identifying specific design levers so organizations can better sequence and evaluate their phishing programs.

  DOI

• Research Note-Breaking Bad Email Habits: Bounding the Impact of Simulated Phishing Campaigns

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Breaking Bad Email Habits: Bounding the Impact of Simulated Phishing Campaigns

  ArXiv.org · 2026-03-04

  articleOpen access

  Simulated phishing campaigns are widely deployed, yet the behavioral data they produce is endogenous: because training is triggered by clicking, the employees receiving intervention have already demonstrated susceptibility. This endogeneity, combined with the difficulty of separating genuine habit formation from stable individual differences, means standard analyses can mischaracterize program effectiveness. In this Research Note, we develop a generalizable analytic framework addressing both biases simultaneously. We utilize marginal structural models (MSMs) to correct for the endogenous, click-triggered assignment of training, while integrating correlated random effects (CRE) to disentangle true state dependence from stable employee heterogeneity. Applying the MSM+CRE estimator to logs from 17 campaigns delivered to university staff (192,840 observations) reveals that analyses ignoring stable differences overstate the causal persistence of clicking; most repeat clicking reflects who employees are, not the effect of recent failures. This persistence is context-dependent, amplifying when successive campaigns share persuasion cues. Teachable-moment features also matter: emotion framing and explicit reporting pitches can largely eliminate persistence, while annotated-email cues modestly exacerbate it. Finally, employees engaging with the education page exhibit greater persistence than those dismissing it, consistent with an emboldening mechanism. We contribute methodologically by integrating MSMs and CRE into a portable framework for analyzing standard simulation logs, and practically by identifying specific design levers so organizations can better sequence and evaluate their phishing programs.

  PublisherOA PDF

• Till Tech Do Us Part: Betrayal Aversion and Its Role in Algorithm Use

  Management Science · 2025-02-17 · 4 citations

  article

  Failing to follow expert advice can have real and dangerous consequences. While any number of factors may lead a decision maker to refuse expert advice, the proliferation of algorithmic experts has further complicated the issue. One potential mechanism that restricts the acceptance of expert advice is betrayal aversion, or the strong dislike for the violation of trust norms. This study explores whether the introduction of expert algorithms in place of human experts can attenuate betrayal aversion and lead to higher overall rates of seeking expert advice. In other words, we ask: are decision makers averse to algorithmic betrayal? The answer to this question is uncertain ex ante. We answer this question through an experimental financial market where there is an identical risk of betrayal from either a human or algorithmic financial advisor. We find that the willingness to delegate to human experts is significantly reduced by betrayal aversion, while no betrayal aversion is exhibited toward algorithmic experts. The impact of betrayal aversion toward financial advisors is considerable: the resulting unwillingness to take the advice of the human expert leads to a 20% decrease in subsequent earnings, while no loss in earnings is observed in the algorithmic expert condition. This study has significant implications for firms, policymakers, and consumers, specifically in the financial services industry. This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection. Funding: This work was supported by National Science Foundation [Grant 1541105]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03510 .

  PublisherDOI

• Smartphone Use, Social Support, and Sleep Health

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• The Impact of AI Chatbot Usage on Collective Problem Solving

  Open Science Framework · 2025-11-10

  otherOpen access1st authorCorresponding

  This study investigates how the integration structure (shared vs. separate AI use) and continuity (continuous vs. intermittent access) of AI chatbot support affects how human teams reason, coordinate, and integrate distributed information.

  PublisherDOI

• Big data in psychology: A framework for research advancement.

  American Psychological Association eBooks · 2024-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• <div> Behavioral Advertising and Consumer Welfare: <span>An Empirical Investigation</span></div>

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Till Tech Do Us Part: Betrayal Aversion and its Role in Algorithm Use

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Nudging when it Matters: Machine Learning-based Nudging to Improve Physical Activity

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Alessandro Acquisti

  23 shared

• Corey M. Angst

  University of Notre Dame

  18 shared

• George Loewenstein

  10 shared

• Brad N. Greenwood

  George Mason University

  7 shared

• Lorrie Faith Cranor

  Carnegie Mellon University

  6 shared

• Florian Schaub

  University of Michigan–Ann Arbor

  6 shared

• Laura Brandimarte

  6 shared

• Sasha Romanosky

  6 shared